Systems and methods for reducing carbon emissions by recommending one or more alternative forms of transportation

ABSTRACT

Method and system for reducing carbon emissions by recommending one or more alternative forms of transportation are disclosed. For example, the method includes collecting user data for a trip made by a user using a form of transportation between a first origination point and a first destination point, determining an amount of carbon emissions generated by the trip based upon the user data related to the form of transportation, determining one or more alternative forms of transportation between the first origination point and first destination point, determining a composite score associated with each alternative form of transportation, the composite score representing a likelihood of acceptance of the respective alternative form of transportation by the user, and selecting a recommended alternative form of transportation from the one or more alternative forms of transportation based at least in part on the composite score associated with each alternative form of transportation.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent Application No. 63/000,874, filed Mar. 27, 2020, incorporated by reference herein in its entirety.

FIELD OF THE DISCLOSURE

Some embodiments of the present disclosure are directed to reducing carbon emissions by recommending one or more alternative forms of transportation to a user. More particularly, certain embodiments of the present disclosure provide systems and methods for reducing carbon emissions by recommending one or more alternative forms of transportation that a user is likely to accept. Merely by way of example, the present disclosure has been applied to recommending one or more alternative forms of transportation that a user is likely to accept by determining a composite score associated with each alternative form of transportation. But it would be recognized that the present disclosure has much broader range of applicability.

BACKGROUND OF THE DISCLOSURE

Numerous route selection techniques exist to assist a vehicle operator in mapping out a route for a potential trip. These techniques typically examine various routes that are available and generate route choices for the vehicle operator based on factors such as shortest distance, fastest travel time, number of scenic views, etc. However, many of these techniques do not consider carbon emissions emitted by a utilized form of transportation. Hence, there remains a need to develop transportation recommendation techniques for determining one or more alternative forms of transportation based on carbon emissions.

BRIEF SUMMARY OF THE DISCLOSURE

Some embodiments of the present disclosure are directed to reducing carbon emissions by recommending one or more alternative forms of transportation to a user. More particularly, certain embodiments of the present disclosure provide methods and systems for reducing carbon emissions by recommending one or more alternative forms of transportation that a user is likely to accept. Merely by way of example, the present disclosure has been applied to recommending one or more alternative forms of transportation that a user is likely to accept by determining a composite score associated with each alternative form of transportation. But it would be recognized that the present disclosure has much broader range of applicability.

According to certain embodiments, a method for reducing carbon emissions by recommending one or more alternative forms of transportation includes collecting user data for a trip made by a user using a form of transportation between a first origination point and a first destination point and determining an amount of carbon emissions generated by the trip based upon the user data related to the form of transportation. The method further includes determining one or more alternative forms of transportation between the first origination point and the first destination point. Each of the one or more alternative forms of transportation estimated to generate less amount of carbon emissions compared to the amount of carbon emissions generated by the form of transportation. Also, the method includes determining a composite score associated with each alternative form of transportation. The composite score representing a likelihood of acceptance of the respective alternative form of transportation by the user. Additionally, the method includes selecting a recommended alternative form of transportation from the one or more alternative forms of transportation based at least in part on the composite score associated with each alternative form of transportation. Moreover, the method further includes presenting the recommended alternative form of transportation to the user.

According to certain embodiments, a computing device for reducing carbon emissions by recommending one or more alternative forms of transportation includes one or more processors and a memory that stores instructions for execution by the one or more processors. The instructions, when executed, cause the one or more processors to collect user data for a trip made by a user using a form of transportation between a first origination point and a first destination point and determine an amount of carbon emissions generated by the trip based upon the user data related to the form of transportation. Further, the instructions, when executed, cause the one or more processors to determine one or more alternative forms of transportation between the first ongination point and the first destination point. Each of the one or more alternative forms of transportation estimated to generate less amount of carbon emissions compared to the amount of carbon emissions generated by the form of transportation. Also, the instructions, when executed, cause the one or more processors to determine a composite score associated with each alternative form of transportation. The composite score representing a likelihood of acceptance of the respective alternative form of transportation by the user. Additionally, the instructions, when executed, cause the one or more processors to select a recommended alternative form of transportation from the one or more alternative forms of transportation based at least in part on the composite score associated with each alternative form of transportation. Moreover, the instructions, when executed, cause the one or more processors to present the recommended alternative form of transportation to the user.

According to certain embodiments, a non-transitory computer-readable medium stores instructions for reducing carbon emissions by recommending one or more alternative forms of transportation. The instructions are executed by one or more processors of a computing device. The non-transitory computer-readable medium includes instructions to collect user data for a trip made by a user using a form of transportation between a first origination point and a first destination point and determine an amount of carbon emissions generated by the trip based upon the user data related to the form of transportation. Further, the non-transitory computer-readable medium includes instructions to determine one or more alternative forms of transportation between the first origination point and the first destination point. Each of the one or more alternative forms of transportation estimated to generate less amount of carbon emissions compared to the amount of carbon emissions generated by the form of transportation. Also, the non-transitory computer-readable medium includes instructions to determine a composite score associated with each alternative form of transportation. The composite score representing a likelihood of acceptance of the respective alternative form of transportation by the user. Additionally, the non-transitory computer-readable medium includes instructions to select a recommended alternative form of transportation from the one or more alternative forms of transportation based at least in part on the composite score associated with each alternative form of transportation. Moreover, the non-transitory computer-readable medium includes instructions to present the recommended alternative form of transportation to the user.

Depending upon the embodiment, one or more benefits may be achieved. These benefits and various additional objects, features and advantages of the present disclosure can be fully appreciated with reference to the detailed description and accompanying drawings that follow.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a simplified flowchart showing a method for reducing carbon emissions by recommending one or more alternative forms of transportation according to certain embodiments of the present disclosure.

FIGS. 2A and 2B are simplified flowchart showing a method for reducing carbon emissions by recommending one or more alternative forms of transportation according to some embodiments of the present disclosure.

FIGS. 3A-3C are simplified flowchart showing a method for reducing carbon emissions by recommending one or more alternative forms of transportation according to certain embodiments of the present disclosure.

FIG. 4 is a simplified diagram showing a system for reducing carbon emissions by recommending one or more alternative forms of transportation according to certain embodiments of the present disclosure.

FIG. 5 is a simplified diagram showing a computing device according to certain embodiments of the present disclosure.

DETAILED DESCRIPTION OF THE DISCLOSURE

Some embodiments of the present disclosure are directed to reducing carbon emissions by recommending one or more alternative forms of transportation to a user. More particularly, certain embodiments of the present disclosure provide methods and systems for reducing carbon emissions by recommending one or more alternative forms of transportation that a user is likely to accept. Merely by way of example, the present disclosure has been applied to recommending one or more alternative forms of transportation that a user is likely to accept by determining a composite score associated with each alternative form of transportation. But it would be recognized that the present disclosure has much broader range of applicability.

I. One or More Methods for Reducing Carbon Emissions by Recommending One or More Alternative Forms of Transportation According to Certain Embodiments

FIG. 1 is a simplified diagram showing a method 100 for reducing carbon emissions by recommending one or more alternative forms of transportation according to certain embodiments of the present disclosure. This diagram is merely an example, which should not unduly limit the scope of the claims. One of ordinary skill in the art would recognize many variations, alternatives, and modifications. In the illustrative embodiment, the method 100 is performed by a computing device (e.g., a server 406). However, it should be appreciated that, in some embodiments, some of the method 100 is performed by any computing device.

The method 100 includes process 102 for collecting user data for a trip made by a user using a form of transportation between a first origination point and a first destination point, process 104 for determining an amount of carbon emissions generated by the trip based upon the user data related to the form of transportation, process 106 for determining one or more alternative forms of transportation between the first origination point and the first destination point, process 108 for determining a composite score associated with each alternative form of transportation, process 110 for selecting a recommended alternative form of transportation from the one or more alternative forms of transportation based at least in part on the composite score associated with each alternative form of transportation, and process 112 for presenting the recommended alternative form of transportation to the user.

Although the above has been shown using a selected group of processes for the method, there can be many alternatives, modifications, and variations. For example, some of the processes may be expanded and/or combined. Other processes may be inserted to those noted above. Depending upon the embodiment, the sequence of processes may be interchanged with others replaced. For example, although the method 100 is described as performed by the computing device above, some or all processes of the method are performed by any computing device or a processor directed by instructions stored in memory. As an example, some or all processes of the method are performed according to instructions stored in a non-transitory computer-readable medium.

Specifically, at the process 102, the user data is collected for one or more trips that have already been made by the user using a form of transportation (e.g., a user's vehicle) between a first origination point and a first destination point according to some embodiments. For example, the user data is associated with the trips that the user has made using the user's vehicle during the previous day, previous week, previous month, previous year, or any combination thereof.

According to certain embodiments, the user data includes information related to time, distance, and/or a user driving behavior associated with each trip made by the user. As an example, the user driving behavior represents a manner in which the user has operated a vehicle. For example, the user driving behavior indicates the user's driving habits and/or driving patterns. As discussed below, the user data may be used to determine an amount of carbon emissions generated by the user's vehicle.

According to some embodiments, the user data is received, obtained, or otherwise collected from one or more sensors associated with the user's vehicle. For example, the one or more sensors include any type and number of accelerometers, gyroscopes, magnetometers, location sensors (e.g., GPS sensors), tilt sensors, yaw rate sensors, speedometers, steering angle sensors, brake sensors, proximity detectors, and/or any other suitable sensors that measure vehicle state and/or operation. In certain embodiments, the one or more sensors are part of or located in the vehicle. In some embodiments, the one or more sensors are part of a computing device (e.g., a mobile device of the user) that is connected to the vehicle while the vehicle is in operation. According to certain embodiments, the user driving data are collected continuously or at predetermined time intervals. According to some embodiments, the user driving data are collected based on a triggering event. For example, the user driving data are collected when each sensor has acquired a threshold amount of sensor measurements. According to other embodiments, the user data may be received, obtained, or otherwise collected from a server (e.g., a server 406) associated with an insurance provider.

At the process 104, the user data is analyzed to determine an amount of carbon emissions generated by the user's vehicle during the trip. As an example, the amount of carbon emissions generated by the user's vehicle during the trip depends on a distance and/or a duration of the trip. For example, the amount of carbon emissions generated by the user's vehicle increases as the distance and/or the duration of the trip increases.

According to some embodiments, the amount of carbon emissions generated by the user's vehicle during the trip is related to a fuel consumption efficiency of the user. For example, the fuel consumption efficiency indicates how fuel has been consumed during the respective trip given one or more user driving features. As an example, the one or more user driving features indicate various driving maneuvers made by the user that can have an impact on the amount of fuel consumed including braking (e.g., excessive braking, sudden braking, braking while reaching a turn, braking while driving in a turn), acceleration (e.g., rapid acceleration, prolonged acceleration, acceleration while driving in a turn, accelerating while exiting a turn), cornering (e.g., sharp turning, swerving), speeding (e.g., cruising, adopting speed limits), lane changing, tailgating, idling, timing of gear shifting, and/or other suitable maneuvers. According to some embodiments, the one or more user driving features are classified by their level of severity (e.g., speed and duration at which a maneuver is performed).

According to certain embodiments, the one or more user driving features include a first set of user driving features that reduce the carbon emissions generated by the user's vehicle (e.g., increase the fuel consumption efficiency) and a second set of user driving features that increase the carbon emissions generated by the user's vehicle (e.g., decrease the fuel consumption efficiency). For example, a type of maneuver belonging to the first set of user driving features includes performing smooth acceleration at moderate rates which tends to increase the fuel consumption efficiency, thereby reducing the carbon emissions. As an example, a type of maneuver belonging to the second set of user driving features includes using excessive braking which tends to decrease fuel consumption efficiency, thereby increasing the carbon emissions. For example, a type of maneuver belonging to the first set of user driving features includes avoiding constant acceleration by remaining in one lane which tends to increase the fuel consumption efficiency, thereby reducing the carbon emissions. As an example, a type of maneuver belonging to the second set of user driving features includes making unnecessary braking and acceleration by tailgating which tends to decrease fuel consumption efficiency, thereby increasing the carbon emissions.

At the process 106, the one or more alternative forms of transportation between the first origination point and the first destination point are determined. Each of the one or more alternative forms of transportation is estimated to generate less amount of carbon emissions compared to the amount of carbon emissions generated by the form of transportation. As an example, the one or more alternative forms of transportation include walking, bicycle, motorcycle, scooter, bus, train, subway, streetcar, boat, and/or airplane.

According to some embodiments, the one or more alternative forms of transportation are determined based upon general data for one or more trips made by other users using one or more alternative forms of transportation that are different from the user's vehicle. In such embodiments, an amount of carbon emissions generated by each of the alternative forms of transportation associate with each trip is determined based at least in part upon the general data.

At the process 108, for each of the one or more alternative forms of transportation, a respective composite score is determined, which represents a likelihood of acceptance of the respective alternative form of transportation by the user. As an example, the composite score is determined as a function of the general data associated with other users, insurance data of the other users provided by one or more insurance providers, and/or a user preference for alternative forms of transportation of the user.

At the process 110, the recommended alternative form of transportation is selected from the one or more alternative forms of transportation based at least in part on the composite score associated with each alternative form of transportation. As an example, the composite score is used to rank the one or more alternative forms of transportation in the order of which the user's likely to adapt the respective alternative form of transportation.

At the process 112, the recommended alternative form of transportation is presented to the user. As an example, the recommended alternative form of transportation is transmitted to a user's mobile device.

FIGS. 2A and 2B are a simplified method for reducing carbon emissions by recommending one or more alternative forms of transportation according to certain embodiments of the present disclosure. This diagram is merely an example, which should not unduly limit the scope of the claims. One of ordinary skill in the art would recognize many variations, alternatives, and modifications. The method 200 includes process 202 for collecting user data for a trip made by a user using a form of transportation between a first origination point and a first destination point, process 204 for determining an amount of carbon emissions generated by the trip based upon the user data related to the form of transportation, process 206 for collecting general data for a plurality of trips made by other users, process 208 for determining an amount of carbon emissions generated by each of the plurality of trips based upon the general data, process 210 for determining one or more alternative forms of transportation between the first origination point and the first destination point, process 212 for determining a composite score associated with each alternative form of transportation, process 214 for selecting a recommended alternative form of transportation from the one or more alternative forms of transportation based at least in part on the composite score associated with each alternative form of transportation, and process 216 for presenting the recommended alternative form of transportation to the user.

Although the above has been shown using a selected group of processes for the method, there can be many alternatives, modifications, and variations. For example, some of the processes may be expanded and/or combined. Other processes may be inserted to those noted above. Depending upon the embodiment, the sequence of processes may be interchanged with others replaced. For example, although the method 100 is described as performed by the computing device above, some or all processes of the method are performed by any computing device or a processor directed by instructions stored in memory. As an example, some or all processes of the method are performed according to instructions stored in a non-transitory computer-readable medium.

Specifically, at the process 202, the user data is collected for one or more trips that have already been made by the user using a form of transportation (e.g., a user's vehicle) between a first origination point and a first destination point according to some embodiments. For example, the user data is associated with the trips that the user has made using the user's vehicle during the previous day, previous week, previous month, previous year, or any combination thereof.

According to certain embodiments, the user data includes information related to time, distance, and/or a user driving behavior associated with each trip made by the user. As an example, the user driving behavior represents a manner in which the user has operated a vehicle. For example, the user driving behavior indicates the user's driving habits and/or driving patterns. As discussed below, the user data may be used to determine an amount of carbon emissions generated by the user's vehicle.

According to some embodiments, the user data is received, obtained, or otherwise collected from one or more sensors associated with the user's vehicle. For example, the one or more sensors include any type and number of accelerometers, gyroscopes, magnetometers, location sensors (e.g., GPS sensors), tilt sensors, yaw rate sensors, speedometers, steering angle sensors, brake sensors, proximity detectors, and/or any other suitable sensors that measure vehicle state and/or operation. In certain embodiments, the one or more sensors are part of or located in the vehicle. In some embodiments, the one or more sensors are part of a computing device (e.g., a mobile device of the user) that is connected to the vehicle while the vehicle is in operation. According to certain embodiments, the user driving data are collected continuously or at predetermined time intervals. According to some embodiments, the user driving data are collected based on a triggering event. For example, the user driving data are collected when each sensor has acquired a threshold amount of sensor measurements. According to other embodiments, the user data may be received, obtained, or otherwise collected from a server (e.g., a server 406) associated with an insurance provider.

At the process 204, the user data is analyzed to determine an amount of carbon emissions generated by the user's vehicle during the trip. As an example, the amount of carbon emissions generated by the user's vehicle during the trip depends on a distance and/or a duration of the trip. For example, the amount of carbon emissions generated by the user's vehicle increases as the distance and/or the duration of the trip increases.

According to some embodiments, the amount of carbon emissions generated by the user's vehicle during the trip is related to a fuel consumption efficiency of the user. For example, the fuel consumption efficiency indicates how fuel has been consumed during the respective trip given one or more user driving features. As an example, the one or more user driving features indicate various driving maneuvers made by the user that can have an impact on the amount of fuel consumed including braking (e.g., excessive braking, sudden braking, braking while reaching a turn, braking while driving in a turn), acceleration (e.g., rapid acceleration, prolonged acceleration, acceleration while driving in a turn, accelerating while exiting a turn), cornering (e.g., sharp turning, swerving), speeding (e.g., cruising, adopting speed limits), lane changing, tailgating, idling, timing of gear shifting, and/or other suitable maneuvers. According to some embodiments, the one or more user driving features are classified by their level of severity (e.g., speed and duration at which a maneuver is performed).

According to certain embodiments, the one or more user driving features include a first set of user driving features that reduce the carbon emissions generated by the user's vehicle (e.g., increase the fuel consumption efficiency) and a second set of user driving features that increase the carbon emissions generated by the user's vehicle (e.g., decrease the fuel consumption efficiency). For example, a type of maneuver belonging to the first set of user driving features includes performing smooth acceleration at moderate rates which tends to increase the fuel consumption efficiency, thereby reducing the carbon emissions. As an example, a type of maneuver belonging to the second set of user driving features includes using excessive braking which tends to decrease fuel consumption efficiency, thereby increasing the carbon emissions. For example, a type of maneuver belonging to the first set of user driving features includes avoiding constant acceleration by remaining in one lane which tends to increase the fuel consumption efficiency, thereby reducing the carbon emissions. As an example, a type of maneuver belonging to the second set of user driving features includes making unnecessary braking and acceleration by tailgating which tends to decrease fuel consumption efficiency, thereby increasing the carbon emissions.

At the process 2X), the general data for a plurality of trips made by other users is collected. As an example, the general data includes utilized forms of transportation, amounts of carbon emissions, costs, and/or durations of trips made by other users. For example, the utilized forms of transportation include walking, bicycle, motorcycle, scooter, bus, train, subway, streetcar, boat, and/or airplane.

According to some embodiments, similar to the user data, the general data includes information related to time, distance, and/or the other users driving behaviors associated with each trip made by the other users. As an example, the other users driving behavior represents a manner in which the other users have operated a vehicle. For example, the other users driving behavior indicates the other users' driving habits and/or driving patterns. As discussed below, the general data may be used to determine an amount of carbon emissions generated by the respective form of the transportation used by the other users.

According to certain embodiments, the plurality of trips made by other users are substantially similar to the trip made by the user, and the utilized forms of transportation used by the other users are different from the form of transportation used by the user (e.g., the user's vehicle). According to some embodiments, a second trip with a origination point and a destination point made by the other user is substantially similar to a first trip with a first origination point and a first destination point made by the user if the second origination point is within a first predetermined distance from the first origination point and a second destination point within a second predetermined distance from the first destination point.

At the process 208, the general data is analyzed to determine an amount of carbon emissions generated by the respective utilized form of transportation during each of the plurality of trips. As an example, the amount of carbon emissions generated by the utilized form of transportation during each of the plurality of trips depends on a distance and/or a duration of the respective trip. For example, the amount of carbon emissions generated by the utilized form of transportation increases as the distance and/or the duration of the trip increases. According to some embodiments, the amount of carbon emissions generated by the utilized form of transportation during the respective trip is related to a fuel consumption efficiency of the other users.

At the process 210, the one or more alternative forms of transportation between the first origination point and the first destination point are determined based upon the general data. As an example, each of the one or more alternative forms of transportation is estimated to generate less amount of carbon emissions compared to the amount of carbon emissions generated by the form of transportation. As an example, the one or more alternative forms of transportation include walking, bicycle, motorcycle, scooter, bus, train, subway, streetcar, boat, and/or airplane.

According to some embodiments, the one or more alternative forms of transportation are determined based upon the general data made by other users using one or more alternative forms of transportation that are different from the form of transportation used by the user (e.g., the user's vehicle). In such embodiments, an amount of carbon emissions generated by each of the alternative forms of transportation associate with each trip is determined based at least in part upon the general data.

At the process 212, for each of the one or more alternative forms of transportation, a respective composite score is determined, which represents a likelihood of acceptance of the respective alternative form of transportation by the user. As an example, the composite score is determined as a function of the general data associated with other users, insurance data of the other users provided by one or more insurance providers, and/or a user preference for alternative forms of transportation of the user.

At the process 214, the recommended alternative form of transportation is selected from the one or more alternative forms of transportation based at least in part on the composite score associated with each alternative form of transportation. As an example, the composite score is used to rank the one or more alternative forms of transportation in the order of which the user's likely to adapt the respective alternative form of transportation.

At the process 216, the recommended alternative form of transportation is presented to the user. As an example, the recommended alternative form of transportation is transmitted to a user's mobile device.

FIGS. 3A-3C are a simplified method for reducing carbon emissions by recommending one or more alternative forms of transportation according to certain embodiments of the present disclosure. This diagram is merely an example, which should not unduly limit the scope of the claims. One of ordinary skill in the art would recognize many variations, alternatives, and modifications.

The method 300 includes process 302 for collecting user data for a trip made by a user using a form of transportation between a first origination point and a first destination point, process 304 for determining an amount of carbon emissions generated by the trip based upon the user data related to the form of transportation, process 306 for collecting general data for a plurality of trips made by other users, process 308 for determining an amount of carbon emissions generated by each of the plurality of trips based upon the general data, process 310 for determining one or more alternative forms of transportation between the first origination point and the first destination point, process 312 for calculating a first score for each one or more alternative forms of transportation based at least in part upon the collected general data, process 314 for collecting insurance data of the other users provided by one or more insurance providers, process 316 for calculating a second score based at least in part upon the insurance data of the other users, process 318 for calculating a third score indicative of a user preference for the respective alternative form of transportation by the user, process 320 for determining a composite score associated with each alternative form of transportation, process 322 for selecting a recommended alternative form of transportation from the one or more alternative forms of transportation based at least in part on the composite score associated with each alternative form of transportation, process 324 for presenting the recommended alternative form of transportation to the user, process 326 for receiving, in response to presenting the recommended alternative form of transportation to the user, a response from the user indicating an acceptance of the recommended alternative form of transportation, process 328 for updating or adjusting, in response to receiving the acceptance of the recommended alternative form of transportation, an amount of insurance discount associated with the user, and process 330 for presenting the recommended alternative form of transportation to the user.

Although the above has been shown using a selected group of processes for the method, there can be many alternatives, modifications, and variations. For example, some of the processes may be expanded and/or combined. Other processes may be inserted to those noted above. Depending upon the embodiment, the sequence of processes may be interchanged with others replaced. For example, although the method 100 is described as performed by the computing device above, some or all processes of the method are performed by any computing device or a processor directed by instructions stored in memory. As an example, some or all processes of the method are performed according to instructions stored in a non-transitory computer-readable medium.

Specifically, at the process 302, the user data is collected for one or more trips that have already been made by the user using a form of transportation (e.g., a user's vehicle) between a first origination point and a first destination point according to some embodiments. For example, the user data is associated with the trips that the user has made using the user's vehicle during the previous day, previous week, previous month, previous year, or any combination thereof.

According to certain embodiments, the user data includes information related to time, distance, and/or a user driving behavior associated with each trip made by the user. As an example, the user driving behavior represents a manner in which the user has operated a vehicle. For example, the user driving behavior indicates the user's driving habits and/or driving patterns. As discussed below, the user data may be used to determine an amount of carbon emissions generated by the user's vehicle.

According to some embodiments, the user data is received, obtained, or otherwise collected from one or more sensors associated with the user's vehicle. For example, the one or more sensors include any type and number of accelerometers, gyroscopes, magnetometers, location sensors (e.g., GPS sensors), tilt sensors, yaw rate sensors, speedometers, steering angle sensors, brake sensors, proximity detectors, and/or any other suitable sensors that measure vehicle state and/or operation. In certain embodiments, the one or more sensors are part of or located in the vehicle. In some embodiments, the one or more sensors are part of a computing device (e.g., a mobile device of the user) that is connected to the vehicle while the vehicle is in operation. According to certain embodiments, the user driving data are collected continuously or at predetermined time intervals. According to some embodiments, the user driving data are collected based on a triggering event. For example, the user driving data are collected when each sensor has acquired a threshold amount of sensor measurements. According to other embodiments, the user data may be received, obtained, or otherwise collected from a server (e.g., a server 406) associated with an insurance provider.

At the process 304, the user data is analyzed to determine an amount of carbon emissions generated by the user's vehicle during the trip. As an example, the amount of carbon emissions generated by the user's vehicle during the trip depends on a distance and/or a duration of the trip. For example, the amount of carbon emissions generated by the user's vehicle increases as the distance and/or the duration of the trip increases.

According to some embodiments, the amount of carbon emissions generated by the user's vehicle during the trip is related to a fuel consumption efficiency of the user. For example, the fuel consumption efficiency indicates how fuel has been consumed during the respective trip given one or more user driving features. As an example, the one or more user driving features indicate various driving maneuvers made by the user that can have an impact on the amount of fuel consumed including braking (e.g., excessive braking, sudden braking, braking while reaching a turn, braking while driving in a turn), acceleration (e.g., rapid acceleration, prolonged acceleration, acceleration while driving in a turn, accelerating while exiting a turn), cornering (e.g., sharp turning, swerving), speeding (e.g., cruising, adopting speed limits), lane changing, tailgating, idling, timing of gear shifting, and/or other suitable maneuvers. According to some embodiments, the one or more user driving features are classified by their level of severity (e.g., speed and duration at which a maneuver is performed).

According to certain embodiments, the one or more user driving features include a first set of user driving features that reduce the carbon emissions generated by the user's vehicle (e.g., increase the fuel consumption efficiency) and a second set of user driving features that increase the carbon emissions generated by the user's vehicle (e.g., decrease the fuel consumption efficiency). For example, a type of maneuver belonging to the first set of user driving features includes performing smooth acceleration at moderate rates which tends to increase the fuel consumption efficiency, thereby reducing the carbon emissions. As an example, a type of maneuver belonging to the second set of user driving features includes using excessive braking which tends to decrease fuel consumption efficiency, thereby increasing the carbon emissions. For example, a type of maneuver belonging to the first set of user driving features includes avoiding constant acceleration by remaining in one lane which tends to increase the fuel consumption efficiency, thereby reducing the carbon emissions. As an example, a type of maneuver belonging to the second set of user driving features includes making unnecessary braking and acceleration by tailgating which tends to decrease fuel consumption efficiency, thereby increasing the carbon emissions.

At the process 306, the general data for a plurality of trips made by other users is collected. As an example, the general data includes utilized forms of transportation, amounts of carbon emissions, costs, and/or durations of trips made by other users. For example, the utilized forms of transportation include walking, bicycle, motorcycle, scooter, bus, train, subway, streetcar, boat, and/or airplane.

According to some embodiments, similar to the user data, the general data includes information related to time, distance, and/or the other users driving behaviors associated with each trip made by the other users. As an example, the other users driving behavior represents a manner in which the other users have operated a vehicle. For example, the other users driving behavior indicates the other users' driving habits and/or driving patterns. As discussed below, the general data may be used to determine an amount of carbon emissions generated by the respective form of the transportation used by the other users.

According to certain embodiments, the plurality of trips made by other users are substantially similar to the trip made by the user, and the utilized forms of transportation used by the other users are different from the form of transportation used by the user (e.g., the user's vehicle). According to some embodiments, a second trip with a origination point and a destination point made by the other user is substantially similar to a first trip with a first origination point and a first destination point made by the user if the second origination point is within a first predetermined distance from the first origination point and a second destination point within a second predetermined distance from the first destination point.

At the process 308, the general data is analyzed to determine an amount of carbon emissions generated by the respective utilized form of transportation during each of the plurality of trips. As an example, the amount of carbon emissions generated by the utilized form of transportation during each of the plurality of trips depends on a distance and/or a duration of the respective trip. For example, the amount of carbon emissions generated by the utilized form of transportation increases as the distance and/or the duration of the trip increases. According to some embodiments, the amount of carbon emissions generated by the utilized form of transportation during the respective trip is related to a fuel consumption efficiency of the other users.

At the process 310, the one or more alternative forms of transportation between the first origination point and the first destination point are determined based upon the general data. As an example, the alternative forms of transportation are selected from the utilized forms of transportation used by the other users based upon the general data. According to some embodiments, each of the one or more alternative forms of transportation is estimated to generate less amount of carbon emissions compared to the amount of carbon emissions generated by the form of transportation used by the user. As an example, the one or more alternative forms of transportation include walking, bicycle, motorcycle, scooter, bus, train, subway, streetcar, boat, and/or airplane.

According to certain embodiments, the one or more alternative forms of transportation that are different from the form of transportation used by the user (e.g., the user's vehicle) are determined based upon the general data made by other users. In such embodiments, an amount of carbon emissions generated by each of the alternative forms of transportation associate with each trip is determined based at least in part upon the general data.

At the process 312, for each of the one or more alternative forms of transportation, a first score is determined based at least in part upon the collected general data. As an example, the general data includes trip information of trips made by other users. According to some embodiments, the trip information relates to a utilized form of transportation, a duration, a distance, driving behaviors, an amount of carbon emission, and/or a cost (e.g., fuel) associated with a respective trip made by another user.

As described above, the one or more alternative forms of transportation are selected from the utilized forms of transportation used by the other users based upon the amount of carbon emissions. In other words, the alternative forms of transportation are forms of transportation that are different from the form of transportation used by the user (e.g., the user's vehicle) and generate less amount of carbon emissions during the substantially the same trip compared to the amount of carbon emissions generated by the form of transportation used by the user. According to some embodiments, for each of the alternative forms of transportation, a respective first score is calculated in order to rank the one or more alternative forms of transportation based upon the general data.

According to certain embodiments, a user preference is applied when calculating the first score for each of the alternative forms of transportation. As an example, the user may prefer a shorter trip time compared to a shorter trip distance when commuting between home and work. In such example, the alternative form of transportation that took less time has a higher first score compared to other alternative forms of transportation. According to some embodiments, the user preference includes a plurality of user preferences in a ranked order. In other embodiments, the user preference may be received from an insurance provider.

According to some embodiments, there may be multiple other users who used the same alternative form of transportation during the substantially similar trip. In such embodiments, the computing device determines an average of the general data (e.g., an average time duration, an average distance) of the multiple other users to calculate the first score associated with the corresponding alternative form of transportation.

At the process 314, the insurance data includes insurance premiums of the other users and/or health of the other users. As an example, the insurance data is obtained from one or more insurance providers associated with other users. According to some embodiments, the insurance data is analyzed in view of the general data to correlate the health and/or the insurance premium of other user with the utilized form of transportation used by the other user. For example, based upon the general data and the insurance data, the computing device may determine that a user who bike to work is likely to have good health (e.g., within a normal body mass index (BMI), no severe medical condition) and/or a lower insurance premium.

At the process 316, for each of the one or more alternative forms of transportation, a second score based at least in part upon the insurance data of the other users. As described above, the one or more alternative forms of transportation are selected from the utilized forms of transportation used by the other users. In other words, each of the alternative forms of transportation is associated with one or more other users. According to some embodiments, the second score of each of the alternative forms of transportation is determined based upon the health and/or the insurance premium associated with other user who used the respective alternative form of transportation during a substantially similar trip.

According to certain embodiments, a user preference is applied when calculating the second score for each of the alternative forms of transportation. As an example, improving the user's health may be more important to the user than reducing an insurance premium. In such example, the alternative form of transportation used by one or more other users who are determined to have good health has a higher second score compared to other alternative forms of transportation. According to some embodiments, the user preference includes a plurality of user preferences in a ranked order. In other embodiments, the user preference may be received from an insurance provider.

According to certain embodiments, there may be multiple other users who used the same alternative form of transportation during the substantially similar trip. In such embodiments, the computing device determines an average of the healthiness of the multiple other users to calculate the second score associated with the corresponding alternative form of transportation.

At the process 318, for each of the one or more alternative forms of transportation, a third score indicative of a user preference for the respective alternative form of transportation by the user. As an example, the user preference includes a ranking of preferred forms of transportation and/or a ranking of attributes. According to some embodiments, the attributes include a cost, time, and carbon emissions associated with trips, a health improvement of the user, and/or a premium reduction of an insurance associated with the user. By considering the user preference, the computing device may select a recommended form of transportation that the user is likely to adapt.

According to some embodiments, the user preference is determined based upon providing queries to the user during and/or subsequent to an insurance application process and/or receiving a user input. According to certain embodiments, the user preference is determined based upon historical data associated with previous trips made by the user. For example, the computing device may determine one or more forms of transportation that were utilized by the user and frequencies of usage of the one or more forms of transportation based upon the historical data.

According to certain embodiments, the user preference is determined using one or more machine learning algorithms, programs, modules, or models based upon the historical data and/or information received from the user.

At the process 320, the composite score for each of the one or more alternative forms of transportation is determined as a function of the first score, the second score, and the third score. The composite score represents a likelihood of acceptance of the respective alternative form of transportation by the user. As an example, the composite score is determined as a function of the general data associated with other users, insurance data of the other users provided by one or more insurance providers, and/or a user preference for alternative forms of transportation of the user.

At the process 322, the recommended alternative form of transportation is selected from the one or more alternative forms of transportation based at least in part on the composite score associated with each alternative form of transportation. As an example, the composite score is used to rank the one or more alternative forms of transportation in the order of which the user's likely to adapt the respective alternative form of transportation.

At the process 324, the recommended alternative form of transportation is presented to the user. As an example, the recommended alternative form of transportation is transmitted to a user's mobile device.

At the process 326, in response to presenting the recommended alternative form of transportation to the user, the response is received from the user indicating an acceptance of the recommended alternative form of transportation. As an example, the response is received from a mobile device of the user.

At the process 328, in response to receiving the acceptance of the recommended alternative form of transportation, the amount of insurance discount associated with the user is updated or adjusted. As an example, if the user agrees to bike to work instead of using the user's usual form of transportation, such as user's vehicle, the user may receive a higher discount from an insurer on a driver's insurance (e.g., for using the user's vehicle less) and/or a health insurance of the user (e.g., for adapting healthier lifestyle).

At the process 330, a notification is transmitted to the user indicating that the amount of insurance discount has been updated or adjusted. As an example, the notification is transmitted to a user's mobile device. According to some embodiments, the notification includes an option for the user to approve or challenge the updated or adjusted amount of insurance discount.

II. One or More Systems for Reducing Carbon Emissions by Recommending One or More Alternative Forms of Transportation According to Certain Embodiments

FIG. 4 is a simplified diagram showing a system for reducing carbon emissions by recommending one or more alternative forms of transportation according to certain embodiments of the present disclosure. This diagram is merely an example, which should not unduly limit the scope of the claims. One of ordinary skill in the art would recognize many variations, alternatives, and modifications. In the illustrative embodiment, the system 400 includes a mobile device 402, a network 404, and a server 406. Although the above has been shown using a selected group of components for the system, there can be many alternatives, modifications, and variations. For example, some of the components may be expanded and/or combined. Other components may be inserted to those noted above. Depending upon the embodiment, the arrangement of components may be interchanged with others replaced.

In various embodiments, the system 400 is used to implement the method 100, the method 200, and/or the method 300. According to certain embodiments, the mobile device 402 is communicatively coupled to the server 406 via the network 404. As an example, the mobile device 402 includes one or more processors 416 (e.g., a central processing unit (CPU), a graphics processing unit (GPU)), a memory 418 (e.g., random-access memory (RAM), read-only memory (ROM), flash memory), a communications unit 420 (e.g., a network transceiver), a display unit 422 (e.g., a touchscreen), and one or more sensors 424 (e.g., an accelerometer, a gyroscope, a magnetometer, a location sensor). For example, the one or more sensors 424 are configured to generate the user data. According to some embodiments, the user data are collected continuously, at predetermined time intervals, and/or based on a triggering event (e.g., when each sensor has acquired a threshold amount of sensor measurements).

In some embodiments, the mobile device 402 is operated by the user. For example, the user installs an application associated with an insurer on the mobile device 402 and allows the application to communicate with the one or more sensors 424 to collect data (e.g., the user data). According to some embodiments, the application collects the data continuously, at predetermined time intervals, and/or based on a triggering event (e.g., when each sensor has acquired a threshold amount of sensor measurements). In certain embodiments, the data is used to determine an amount of carbon emissions generated by the user's vehicle during a particular trip in the method 100, the method 200, and/or the method 300. As an example, the data represents the user's driving behaviors.

According to certain embodiments, the collected data are stored in the memory 418 before being transmitted to the server 406 using the communications unit 422 via the network 404 (e.g., via a local area network (LAN), a wide area network (WAN), the Internet). In some embodiments, the collected data are transmitted directly to the server 406 via the network 404. In certain embodiments, the collected data are transmitted to the server 406 via a third party. For example, a data monitoring system stores any and all data collected by the one or more sensors 424 and transmits those data to the server 406 via the network 404 or a different network.

According to certain embodiments, the server 406 includes a processor 430 (e.g., a microprocessor, a microcontroller), a memory 432, a communications unit 434 (e.g., a network transceiver), and a data storage 436 (e.g., one or more databases). In some embodiments, the server 406 is a single server, while in certain embodiments, the server 406 includes a plurality of servers with distributed processing. As an example, in FIG. 4 , the data storage 436 is shown to be part of the server 406. In some embodiments, the data storage 436 is a separate entity coupled to the server 406 via a network such as the network 404. In certain embodiments, the server 406 includes various software applications stored in the memory 432 and executable by the processor 430. For example, these software applications include specific programs, routines, or scripts for performing functions associated with the method 100, the method 200, and/or the method 300. As an example, the software applications include general-purpose software applications for data processing, network communication, database management, web server operation, and/or other functions typically performed by a server.

According to various embodiments, the server 406 receives, via the network 404, the user data collected by the one or more sensors 424 from the application using the communications unit 434 and stores the data in the data storage 436. For example, the server 406 then processes the data to perform one or more processes of the method 100, one or more processes of the method 200, and/or one or more processes of the method 300.

According to certain embodiments, the recommended alternative form of transportation in the method 100, the method 200, and/or the method 300 is transmitted to the mobile device 402, via the network 404, to be provided (e.g., displayed) to the user via the display unit 422.

According to some embodiments, the notification indicating that the amount of insurance discount has been updated or adjusted in the method 300 is transmitted to the mobile device 402, via the network 404, to be provided (e.g., displayed) to the user via the display unit 422.

In some embodiments, one or more processes of the method 100, one or more processes of the method 200, and/or one or more processes of the method 300 are performed by the mobile device 402. For example, the processor 416 of the mobile device 402 processes the data collected by the one or more sensors 424 to perform one or more processes of the method 100, one or more processes of the method 200, and/or one or more processes of the method 300.

III. One or More Computer Devices According to Various Embodiments

FIG. 5 is a simplified diagram showing a computer device 500, according to various embodiments of the present disclosure. This diagram is merely an example, which should not unduly limit the scope of the claims. One of ordinary skill in the art would recognize many variations, alternatives, and modifications. In some examples, the computer device 500 includes a processing unit 502, a memory unit 504, an input unit 506, an output unit 508, and a communication unit 510. In various examples, the computer device 500 is configured to be in communication with a user 520 and/or a storage device 522. In certain examples, the system computer device 500 is configured according to the system 400 of FIG. 4 to implement the method 100 of FIG. 1 , the method 200 of FIG. 2 , and/or the method 300 of FIG. 3. Although the above has been shown using a selected group of components, there can be many alternatives, modifications, and variations. In some examples, some of the components may be expanded and/or combined. Some components may be removed. Other components may be inserted to those noted above. Depending upon the embodiment, the arrangement of components may be interchanged with others replaced.

In various embodiments, the processing unit 502 is configured for executing instructions, such as instructions to implement the method 100 of FIG. 1 , the method 200 of FIG. 2 , and/or the method 300 of FIG. 3 . In some embodiments, executable instructions may be stored in the memory unit 504. In some examples, the processing unit 502 includes one or more processing units (e.g., in a multi-core configuration). In certain examples, the processing unit 502 includes and/or is communicatively coupled to one or more modules for implementing the systems and methods described in the present disclosure. In some examples, the processing unit 502 is configured to execute instructions within one or more operating systems, such as UNIX, LINUX, Microsoft Windows®, etc. In certain examples, upon initiation of a computer-implemented method, one or more instructions is executed during initialization. In some examples, one or more operations is executed to perform one or more processes described herein. In certain examples, an operation may be general or specific to a particular programming language (e.g., C, C#, C++, Java, or other suitable programming languages, etc.). In various examples, the processing unit 502 is configured to be operatively coupled to the storage device 522, such as via an on-board storage unit 512.

In various embodiments, the memory unit 504 includes a device allowing information, such as executable instructions and/or other data to be stored and retrieved. In some examples, memory unit 504 includes one or more computer readable media. In some embodiments, data stored in memory unit 504 include computer readable instructions for providing a user interface, such as to the user 504, via the output unit 508. In some examples, a user interface includes a web browser and/or a client application. In various examples, a web browser enables one or more users, such as the user 504, to display and/or interact with media and/or other information embedded on a web page and/or a website. In certain examples, the memory unit 504 include computer readable instructions for receiving and processing an input, such as from the user 504, via the input unit 506. In certain examples, the memory unit 504 includes random access memory (RAM) such as dynamic RAM (DRAM) or static RAM (SRAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), and/or non-volatile RAM (NVRAN).

In various embodiments, the input unit 506 is configured to receive input, such as from the user 504. In some examples, the input unit 506 includes a keyboard, a pointing device, a mouse, a stylus, a touch sensitive panel (e.g., a touch pad or a touch screen), a gyroscope, an accelerometer, a position detector (e.g., a Global Positioning System), and/or an audio input device. In certain examples, the input unit 506, such as a touch screen of the input unit, is configured to function as both the input unit and the output unit.

In various embodiments, the output unit 508 includes a media output unit configured to present information to the user 504. In some embodiments, the output unit 508 includes any component capable of conveying information to the user 504. In certain embodiments, the output unit 508 includes an output adapter, such as a video adapter and/or an audio adapter. In various examples, the output unit 508, such as an output adapter of the output unit, is operatively coupled to the processing unit 502 and/or operatively coupled to an presenting device configured to present the information to the user, such as via a visual display device (e.g., a liquid crystal display (LCD), a light emitting diode (LED) display, an organic light emitting diode (OLED) display, a cathode ray tube (CRT) display, an “electronic ink” display, a projected display, etc.) or an audio display device (e.g., a speaker arrangement or headphones).

In various embodiments, the communication unit 510 is configured to be communicatively coupled to a remote device. In some examples, the communication unit 510 includes a wired network adapter, a wireless network adapter, a wireless data transceiver for use with a mobile phone network (e.g., Global System for Mobile communications (GSM), 3G, 4G, or Bluetooth), and/or other mobile data networks (e.g., Worldwide Interoperability for Microwave Access (WIMAX)). In certain examples, other types of short-range or long-range networks may be used. In some examples, the communication unit 510 is configured to provide email integration for communicating data between a server and one or more clients.

In various embodiments, the storage unit 512 is configured to enable communication between the computer device 500, such as via the processing unit 502, and an external storage device 522. In some examples, the storage unit 512 is a storage interface. In certain examples, the storage interface is any component capable of providing the processing unit 502 with access to the storage device 522. In various examples, the storage unit 512 includes an Advanced Technology Attachment (ATA) adapter, a Serial ATA (SATA) adapter, a Small Computer System Interface (SCSI) adapter, a RAID controller, a SAN adapter, a network adapter, and/or any other component capable of providing the processing unit 502 with access to the storage device 522.

In some examples, the storage device 522 includes any computer-operated hardware suitable for storing and/or retrieving data. In certain examples, storage device 522 is integrated in the computer device 500. In some examples, the storage device 522 includes a database, such as a local database or a cloud database. In certain examples, the storage device 522 includes one or more hard disk drives. In various examples, the storage device is external and is configured to be accessed by a plurality of server systems. In certain examples, the storage device includes multiple storage units such as hard disks or solid state disks in a redundant array of inexpensive disks (RAID) configuration. In some examples, the storage device 522 includes a storage area network (SAN) and/or a network attached storage (NAS) system.

IV. Examples of Certain Embodiments of the Present Disclosure

According to certain embodiments, a method for reducing carbon emissions by recommending one or more alternative forms of transportation includes collecting user data for a trip made by a user using a form of transportation between a first origination point and a first destination point and determining an amount of carbon emissions generated by the trip based upon the user data related to the form of transportation. The method further includes determining one or more alternative forms of transportation between the first origination point and the first destination point Each of the one or more alternative forms of transportation estimated to generate less amount of carbon emissions compared to the amount of carbon emissions generated by the form of transportation. Also, the method includes determining a composite score associated with each alternative form of transportation. The composite score representing a likelihood of acceptance of the respective alternative form of transportation by the user. Additionally, the method includes selecting a recommended alternative form of transportation from the one or more alternative forms of transportation based at least in part on the composite score associated with each alternative form of transportation. Moreover, the method further includes presenting the recommended alternative form of transportation to the user. For example, the method is implemented according to at least FIG. 1 . FIGS. 2A and 2B, and/or FIGS. 3A-3C.

According to certain embodiments, a computing device for reducing carbon emissions by recommending one or more alternative forms of transportation includes one or more processors and a memory that stores instructions for execution by the one or more processors. The instructions, when executed, cause the one or more processors to collect user data for a trip made by a user using a form of transportation between a first origination point and a first destination point and determine an amount of carbon emissions generated by the trip based upon the user data related to the form of transportation. Further, the instructions, when executed, cause the one or more processors to determine one or more alternative forms of transportation between the first origination point and the first destination point. Each of the one or more alternative forms of transportation estimated to generate less amount of carbon emissions compared to the amount of carbon emissions generated by the form of transportation. Also, the instructions, when executed, cause the one or more processors to determine a composite score associated with each alternative form of transportation. The composite score representing a likelihood of acceptance of the respective alternative form of transportation by the user. Additionally, the instructions, when executed, cause the one or more processors to select a recommended alternative form of transportation from the one or more alternative forms of transportation based at least in part on the composite score associated with each alternative form of transportation. Moreover, the instructions, when executed, cause the one or more processors to present the recommended alternative form of transportation to the user. For example, the computing device (e.g., the server 406) is implemented according to at least FIG. 4 .

According to certain embodiments, a non-transitory computer-readable medium stores instructions for reducing carbon emissions by recommending one or more alternative forms of transportation. The instructions are executed by one or more processors of a computing device. The non-transitory computer-readable medium includes instructions to collect user data for a trip made by a user using a form of transportation between a first origination point and a first destination point and determine an amount of carbon emissions generated by the trip based upon the user data related to the form of transportation. Further, the non-transitory computer-readable medium includes instructions to determine one or more alternative forms of transportation between the first origination point and the first destination point. Each of the one or more alternative forms of transportation estimated to generate less amount of carbon emissions compared to the amount of carbon emissions generated by the form of transportation. Also, the non-transitory computer-readable medium includes instructions to determine a composite score associated with each alternative form of transportation. The composite score representing a likelihood of acceptance of the respective alternative form of transportation by the user. Additionally, the non-transitory computer-readable medium includes instructions to select a recommended alternative form of transportation from the one or more alternative forms of transportation based at least in part on the composite score associated with each alternative form of transportation. Moreover, the non-transitory computer-readable medium includes instructions to present the recommended alternative form of transportation to the user. For example, the non-transitory computer-readable medium is implemented according to at least FIG. 1 , FIGS. 2A and 2B, FIGS. 3A-3C, and/or FIG. 4 .

V. Examples of Machine Learning According to Certain Embodiments

According to some embodiments, a processor or a processing element may be trained using supervised machine learning and/or unsupervised machine learning, and the machine learning may employ an artificial neural network, which, for example, may be a convolutional neural network, a recurrent neural network, a deep learning neural network, a reinforcement learning module or program, or a combined learning module or program that learns in two or more fields or areas of interest. Machine learning may involve identifying and recognizing patterns in existing data in order to facilitate making predictions for subsequent data models may be created based upon example inputs in order to make valid and reliable predictions for novel inputs.

According to certain embodiments, machine learning programs may be trained by inputting sample data sets or certain data into the programs, such as images, object statistics and information, historical estimates, and/or actual repair costs. The machine learning programs may utilize deep learning algorithms that may be primarily focused on pattern recognition and may be trained after processing multiple examples. The machine learning programs may include Bayesian Program Learning (BPL), voice recognition and synthesis, image or object recognition, optical character recognition, and/or natural language processing. The machine learning programs may also include natural language processing, semantic analysis, automatic reasoning, and/or other types of machine learning.

According to some embodiments, supervised machine learning techniques and/or unsupervised machine learning techniques may be used. In supervised machine learning, a processing element may be provided with example inputs and their associated outputs and may seek to discover a general rule that maps inputs to outputs, so that when subsequent novel inputs are provided the processing element may, based upon the discovered rule, accurately predict the correct output. In unsupervised machine learning, the processing element may need to find its own structure in unlabeled example inputs.

VI. Additional Considerations According to Certain Embodiments

For example, some or all components of various embodiments of the present disclosure each are, individually and/or in combination with at least another component, implemented using one or more software components, one or more hardware components, and/or one or more combinations of software and hardware components. As an example, some or all components of various embodiments of the present disclosure each are, individually and/or in combination with at least another component, implemented in one or more circuits, such as one or more analog circuits and/or one or more digital circuits. For example, while the embodiments described above refer to particular features, the scope of the present disclosure also includes embodiments having different combinations of features and embodiments that do not include all of the described features. As an example, various embodiments and/or examples of the present disclosure can be combined.

Additionally, the methods and systems described herein may be implemented on many different types of processing devices by program code comprising program instructions that are executable by the device processing subsystem. The software program instructions may include source code, object code, machine code, or any other stored data that is operable to cause a processing system to perform the methods and operations described herein. Certain implementations may also be used, however, such as firmware or even appropriately designed hardware configured to perform the methods and systems described herein.

The systems' and methods' data (e.g., associations, mappings, data input, data output, intermediate data results, final data results) may be stored and implemented in one or more different types of computer-implemented data stores, such as different types of storage devices and programming constructs (e.g., RAM, ROM, EEPROM. Flash memory, flat files, databases, programming data structures, programming variables, IF-THEN (or similar type) statement constructs, application programming interface). It is noted that data structures describe formats for use in organizing and storing data in databases, programs, memory, or other computer-readable media for use by a computer program.

The systems and methods may be provided on many different types of computer-readable media including computer storage mechanisms (e.g., CD-ROM, diskette, RAM, flash memory, computer's hard drive, DVD) that contain instructions (e.g., software) for use in execution by a processor to perform the methods' operations and implement the systems described herein. The computer components, software modules, functions, data stores and data structures described herein may be connected directly or indirectly to each other in order to allow the flow of data needed for their operations. It is also noted that a module or processor includes a unit of code that performs a software operation, and can be implemented for example as a subroutine unit of code, or as a software function unit of code, or as an object (as in an object-oriented paradigm), or as an applet, or in a computer script language, or as another type of computer code. The software components and/or functionality may be located on a single computer or distributed across multiple computers depending upon the situation at hand.

The computing system can include mobile devices and servers A mobile device and server are generally remote from each other and typically interact through a communication network. The relationship of mobile device and server arises by virtue of computer programs running on the respective computers and having a mobile device-server relationship to each other.

This specification contains many specifics for particular embodiments. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination. Moreover, although features may be described above as acting in certain combinations, one or more features from a combination can in some cases be removed from the combination, and a combination may, for example, be directed to a subcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Moreover, the separation of various system components in the embodiments described above should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

Although specific embodiments of the present disclosure have been described, it will be understood by those of skill in the art that there are other embodiments that are equivalent to the described embodiments. Accordingly, it is to be understood that the present disclosure is not to be limited by the specific illustrated embodiments. 

What is claimed is:
 1. A computer-implemented method for reducing carbon emissions by recommending one or more alternative forms of transportation, the method comprising: collecting, by a computing device, user data for a trip made by a user using a form of transportation between a first origination point and a first destination point; determining, by the computing device, an amount of carbon emissions generated by the trip based upon the user data related to the form of transportation; determining, by the computing device, one or more alternative forms of transportation between the first origination point and the first destination point, each of the one or more alternative forms of transportation estimated to generate less amount of carbon emissions compared to the amount of carbon emissions generated by the form of transportation; determining, by the computing device, a composite score associated with each alternative form of transportation, the composite score representing a likelihood of acceptance of the respective alternative form of transportation by the user; selecting, by the computing device, a recommended alternative form of transportation from the one or more alternative forms of transportation based at least in part on the composite score associated with each alternative form of transportation; and presenting, by the computer device, the recommended alternative form of transportation to the user.
 2. The method of claim 1, wherein the one or more alternative forms of transportation include at least one selected from a group consisting of walking, bicycle, motorcycle, scooter, bus, train, subway, streetcar, boat, and airplane.
 3. The method of claim 1, wherein determining the one or more alternative forms of transportation between the first origination point and the first destination point includes: collecting, by the computing device, general data for a plurality of trips made by other users, the plurality of trips being substantially similar to the trip made by the user, the general data including a utilized form of transportation, an amount of carbon emission, a cost, and a duration of a respective trip; determining, by the computing device, an amount of carbon emissions generated by each of the plurality of trips based upon the general data.
 4. The method of claim 3, wherein the plurality of trips that are substantially similar to the trip made by the user include one or more trips with a respective origination point within a first predetermined distance from the first origination point and a respective destination point within a second predetermined distance from the first destination point.
 5. The method of claim 1, wherein determining the composite score associated with each alternative form of transportation includes: calculating, by the computing device, a first score for each one or more alternative forms of transportation based at least in part upon the collected general data.
 6. The method of claim 5, wherein determining the composite score associated with each alternative form of transportation includes: collecting, by the computing device, insurance data of the other users provided by one or more insurance providers, the insurance data including at least one selected from a group consisting of insurance premium of the other users and health of the other users, and calculating, by the computing device, a second score based at least in part upon the insurance data of the other users.
 7. The method of claim 6, wherein determining the composite score associated with each alternative form of transportation includes: calculating, by the computing device, a third score indicative of a user preference for the respective alternative form of transportation by the user; and determining, by the computing device, a composite score based at least in part upon the first score, the second score, and the third score.
 8. The method of claim 7, wherein the user preference includes at least one selected from a group consisting of a ranking of preferred forms of transportation and a ranking of attributes: wherein the attributes include at least one selected from a group consisting of a cost, time, and carbon emissions associated with trips, a health improvement of the user, and a premium reduction of an insurance associated with the user.
 9. The method of claim 8, wherein the calculating, by the computing device, a third score indicative of a user preference for the respective alternative form of transportation by the user includes: determining the user preference based at least in part upon a user input and historical data associated with previous trips made by the user.
 10. The method of claim 1, further comprising: receiving, in response to presenting the recommended alternative form of transportation to the user and by the computing device, a response from the user indicating an acceptance of the recommended alternative form of transportation; in response to receiving the acceptance of the recommended alternative form of transportation, updating or adjusting, by the computing device, an amount of insurance discount associated with the user; and transmitting, by the computing device, a notification to the user indicating that the amount of insurance discount has been updated or adjusted.
 11. A computing device for reducing carbon emissions by recommending one or more alternative forms of transportation, the computing device comprising: a processor; and a memory having a plurality of instructions stored thereon that, when executed by the processor, causes the computing device to: collect user data for a trip made by a user using a form of transportation between a first origination point and a first destination point; determine an amount of carbon emissions generated by the trip based upon the user data related to the form of transportation; determine one or more alternative forms of transportation between the first origination point and the first destination point, each of the one or more alternative forms of transportation estimated to generate less amount of carbon emissions compared to the amount of carbon emissions generated by the form of transportation; determine a composite score associated with each alternative form of transportation, the composite score representing a likelihood of acceptance of the respective alternative form of transportation by the user; select a recommended alternative form of transportation from the one or more alternative forms of transportation based at least in part on the composite score associated with each alternative form of transportation; and present the recommended alternative form of transportation to the user.
 12. The computing device of claim 11, wherein the one or more alternative forms of transportation include at least one selected from a group consisting of walking, bicycle, motorcycle, scooter, bus, train, subway, streetcar, boat, and airplane.
 13. The computing device of claim 11, to determine the one or more alternative forms of transportation between the first origination point and the first destination point includes to: collect general data for a plurality of trips made by other users, the plurality of trips being substantially similar to the trip made by the user, the general data including a utilized form of transportation, an amount of carbon emission, a cost, and a duration of a respective trip; determine an amount of carbon emissions generated by each of the plurality of trips based upon the general data.
 14. The computing device of claim 13, wherein the plurality of trips that are substantially similar to the trip made by the user include one or more trips with a respective origination point within a first predetermined distance from the first origination point and a respective destination point within a second predetermined distance from the first destination point.
 15. The computing device of claim 11, wherein to determine the composite score associated with each alternative form of transportation includes to calculate a first score for each one or more alternative forms of transportation based at least in part upon the collected general data.
 16. The computing device of claim 15, wherein to determine the composite score associated with each alternative form of transportation includes to: collect insurance data of the other users provided by one or more insurance providers, the insurance data including aw least one selected from a group consisting of insurance premium of the other users and health of the other users; and calculate a second score based at least in part upon the insurance data of the other users.
 17. The computing device of claim 16, wherein to determine the composite score associated with each alternative form of transportation includes to: calculate a third score indicative of a user preference for the respective alternative form of transportation by the user; and determine a composite score based at least in part upon the first score, the second score, and the third score.
 18. The computing device of claim 17, wherein the user preference includes at least one selected from a group consisting of a ranking of preferred forms of transportation and a ranking of attributes; wherein the attributes include at least one selected from a group consisting of a cost, time, and carbon emissions associated with trips, a health improvement of the user, and a premium reduction of an insurance associated with the user.
 19. The computing device of claim 18, wherein to calculate the third score indicative of a user preference for the respective alternative form of transportation by the user includes to: determine the user preference based at least in part upon a user input and historical data associated with previous trips made by the user.
 20. The computing device of claim 11, wherein the plurality of instructions, when executed, further cause the computing device to: receive, in response to presenting the recommended alternative form of transportation to the user and by the computing device, a response from the user indicating an acceptance of the recommended alternative form of transportation; in response to receiving the acceptance of the recommended alternative form of transportation, update or adjust an amount of insurance discount associated with the user; and transmit a notification to the user indicating that the amount of insurance discount has been updated or adjusted. 